Overview

Dataset statistics

Number of variables47
Number of observations21251
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 MiB
Average record size in memory369.0 B

Variable types

Numeric7
Boolean1
Categorical39

Alerts

shelf_price is highly overall correlated with pct_disc and 1 other fieldsHigh correlation
pct_disc is highly overall correlated with shelf_price and 1 other fieldsHigh correlation
pct_retail_disc is highly overall correlated with shelf_price and 2 other fieldsHigh correlation
mailer_D is highly overall correlated with pct_retail_discHigh correlation
marital_status_A is highly overall correlated with hhsize_ordinalHigh correlation
hhcomp_2 Adults Kids is highly overall correlated with kid_category_1 and 2 other fieldsHigh correlation
hhcomp_2 Adults No Kids is highly overall correlated with kid_category_None/Unknown and 1 other fieldsHigh correlation
hhcomp_Single Female is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_1 is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
kid_category_2 is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_3+ is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_None/Unknown is highly overall correlated with hhcomp_2 Adults Kids and 3 other fieldsHigh correlation
hhsize_ordinal is highly overall correlated with marital_status_A and 7 other fieldsHigh correlation
campaign_6.0 is highly overall correlated with description_TypeCHigh correlation
campaign_8.0 is highly overall correlated with description_TypeAHigh correlation
campaign_13.0 is highly overall correlated with description_TypeAHigh correlation
campaign_18.0 is highly overall correlated with description_TypeAHigh correlation
campaign_29.0 is highly overall correlated with description_TypeBHigh correlation
description_TypeA is highly overall correlated with campaign_8.0 and 2 other fieldsHigh correlation
description_TypeB is highly overall correlated with campaign_29.0High correlation
description_TypeC is highly overall correlated with campaign_6.0High correlation
display_1 is highly imbalanced (99.3%)Imbalance
display_2 is highly imbalanced (91.2%)Imbalance
display_3 is highly imbalanced (93.5%)Imbalance
display_4 is highly imbalanced (98.8%)Imbalance
display_5 is highly imbalanced (99.3%)Imbalance
display_6 is highly imbalanced (95.1%)Imbalance
display_7 is highly imbalanced (90.6%)Imbalance
display_9 is highly imbalanced (96.2%)Imbalance
display_A is highly imbalanced (99.4%)Imbalance
mailer_C is highly imbalanced (98.6%)Imbalance
mailer_D is highly imbalanced (71.9%)Imbalance
mailer_H is highly imbalanced (93.1%)Imbalance
mailer_J is highly imbalanced (95.3%)Imbalance
homeowner_Probable Owner is highly imbalanced (88.7%)Imbalance
homeowner_Probable Renter is highly imbalanced (95.3%)Imbalance
homeowner_Renter is highly imbalanced (76.8%)Imbalance
hhcomp_1 Adult Kids is highly imbalanced (66.5%)Imbalance
kid_category_2 is highly imbalanced (53.6%)Imbalance
kid_category_3+ is highly imbalanced (52.4%)Imbalance
campaign_6.0 is highly imbalanced (99.6%)Imbalance
campaign_8.0 is highly imbalanced (61.1%)Imbalance
campaign_13.0 is highly imbalanced (60.5%)Imbalance
campaign_18.0 is highly imbalanced (61.3%)Imbalance
campaign_29.0 is highly imbalanced (94.1%)Imbalance
campaign_30.0 is highly imbalanced (99.8%)Imbalance
description_TypeB is highly imbalanced (94.1%)Imbalance
description_TypeC is highly imbalanced (99.6%)Imbalance
Unnamed: 0 has unique valuesUnique
pct_disc has 7551 (35.5%) zerosZeros
pct_retail_disc has 7567 (35.6%) zerosZeros
pct_coupon_disc has 20956 (98.6%) zerosZeros

Reproduction

Analysis started2023-05-28 09:00:36.767904
Analysis finished2023-05-28 09:01:00.419244
Duration23.65 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct21251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17181.748
Minimum16
Maximum34388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-05-28T11:01:00.560389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile1846.5
Q18601.5
median16836
Q325946.5
95-th percentile32358.5
Maximum34388
Range34372
Interquartile range (IQR)17345

Descriptive statistics

Standard deviation9874.5023
Coefficient of variation (CV)0.57470884
Kurtosis-1.2337394
Mean17181.748
Median Absolute Deviation (MAD)8673
Skewness0.023473041
Sum3.6512932 × 108
Variance97505796
MonotonicityStrictly increasing
2023-05-28T11:01:00.718357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1
 
< 0.1%
23171 1
 
< 0.1%
23169 1
 
< 0.1%
23168 1
 
< 0.1%
23167 1
 
< 0.1%
23166 1
 
< 0.1%
23165 1
 
< 0.1%
23164 1
 
< 0.1%
23163 1
 
< 0.1%
23162 1
 
< 0.1%
Other values (21241) 21241
> 99.9%
ValueCountFrequency (%)
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
34388 1
< 0.1%
34387 1
< 0.1%
34386 1
< 0.1%
34385 1
< 0.1%
34384 1
< 0.1%
34383 1
< 0.1%
34382 1
< 0.1%
34381 1
< 0.1%
34380 1
< 0.1%
34379 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
False
14083 
True
7168 
ValueCountFrequency (%)
False 14083
66.3%
True 7168
33.7%
2023-05-28T11:01:00.859222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

shelf_price
Real number (ℝ)

Distinct74
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.156933
Minimum0.35
Maximum6.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-05-28T11:01:00.984655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile0.39
Q10.4
median0.79
Q30.79
95-th percentile3.59
Maximum6.59
Range6.24
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation1.1046807
Coefficient of variation (CV)0.95483548
Kurtosis3.6136688
Mean1.156933
Median Absolute Deviation (MAD)0.39
Skewness1.9435785
Sum24585.983
Variance1.2203194
MonotonicityNot monotonic
2023-05-28T11:01:01.113138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.79 8494
40.0%
0.4 4451
20.9%
0.39 2120
 
10.0%
2.79 946
 
4.5%
0.4 577
 
2.7%
2.49 528
 
2.5%
2.99 434
 
2.0%
3.59 409
 
1.9%
1.49 389
 
1.8%
0.39 311
 
1.5%
Other values (64) 2592
 
12.2%
ValueCountFrequency (%)
0.35 1
 
< 0.1%
0.39 311
 
1.5%
0.39 2120
10.0%
0.4 577
 
2.7%
0.4 4451
20.9%
0.4 11
 
0.1%
0.44 1
 
< 0.1%
0.48 17
 
0.1%
0.5 8
 
< 0.1%
0.59 15
 
0.1%
ValueCountFrequency (%)
6.59 15
 
0.1%
5.79 245
1.2%
4.99 60
 
0.3%
4.89 2
 
< 0.1%
4.89 32
 
0.2%
4.49 56
 
0.3%
4.47 2
 
< 0.1%
4.47 3
 
< 0.1%
4.18 17
 
0.1%
4.18 3
 
< 0.1%

pct_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct227
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15831646
Minimum0
Maximum0.90909091
Zeros7551
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-05-28T11:01:01.235990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.16666667
Q30.24050633
95-th percentile0.36708861
Maximum0.90909091
Range0.90909091
Interquartile range (IQR)0.24050633

Descriptive statistics

Standard deviation0.14584127
Coefficient of variation (CV)0.92120091
Kurtosis0.19144625
Mean0.15831646
Median Absolute Deviation (MAD)0.14594128
Skewness0.59268828
Sum3364.3831
Variance0.021269675
MonotonicityNot monotonic
2023-05-28T11:01:01.361695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7551
35.5%
0.2405063291 5743
27.0%
0.3670886076 1628
 
7.7%
0.1039426523 662
 
3.1%
0.4936708861 525
 
2.5%
0.3288590604 377
 
1.8%
0.08032128514 333
 
1.6%
0.175 290
 
1.4%
0.1003344482 248
 
1.2%
0.2228412256 236
 
1.1%
Other values (217) 3658
17.2%
ValueCountFrequency (%)
0 7551
35.5%
0.01497005988 15
 
0.1%
0.02044989775 1
 
< 0.1%
0.04545454545 17
 
0.1%
0.05527638191 38
 
0.2%
0.05974842767 1
 
< 0.1%
0.06147540984 1
 
< 0.1%
0.06688963211 1
 
< 0.1%
0.07063197026 11
 
0.1%
0.07434944238 17
 
0.1%
ValueCountFrequency (%)
0.9090909091 1
 
< 0.1%
0.8938547486 1
 
< 0.1%
0.8734177215 25
0.1%
0.7890295359 3
 
< 0.1%
0.7751937984 1
 
< 0.1%
0.746835443 12
0.1%
0.72899729 1
 
< 0.1%
0.721448468 3
 
< 0.1%
0.719665272 2
 
< 0.1%
0.7046413502 3
 
< 0.1%

pct_retail_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct143
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15332965
Minimum-0
Maximum0.70234114
Zeros7567
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-05-28T11:01:01.487697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median0.16317992
Q30.24050633
95-th percentile0.36708861
Maximum0.70234114
Range0.70234114
Interquartile range (IQR)0.24050633

Descriptive statistics

Standard deviation0.13754279
Coefficient of variation (CV)0.89703976
Kurtosis-0.88070498
Mean0.15332965
Median Absolute Deviation (MAD)0.13742129
Skewness0.33565764
Sum3258.4084
Variance0.01891802
MonotonicityNot monotonic
2023-05-28T11:01:01.629176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 7567
35.6%
0.2405063291 5808
27.3%
0.3670886076 1642
 
7.7%
0.1039426523 697
 
3.3%
0.4936708861 487
 
2.3%
0.3288590604 375
 
1.8%
0.08032128514 353
 
1.7%
0.175 305
 
1.4%
0.2228412256 251
 
1.2%
0.1003344482 251
 
1.2%
Other values (133) 3515
16.5%
ValueCountFrequency (%)
-0 7567
35.6%
0.01497005988 15
 
0.1%
0.02044989775 1
 
< 0.1%
0.04545454545 17
 
0.1%
0.05527638191 38
 
0.2%
0.05974842767 1
 
< 0.1%
0.06147540984 1
 
< 0.1%
0.06688963211 1
 
< 0.1%
0.07063197026 12
 
0.1%
0.07434944238 17
 
0.1%
ValueCountFrequency (%)
0.7023411371 1
 
< 0.1%
0.6711409396 1
 
< 0.1%
0.4936708861 487
2.3%
0.4936708861 39
 
0.2%
0.4285714286 13
 
0.1%
0.4202898551 4
 
< 0.1%
0.4178272981 1
 
< 0.1%
0.3920972644 2
 
< 0.1%
0.373433584 4
 
< 0.1%
0.3717472119 1
 
< 0.1%

pct_coupon_disc
Real number (ℝ)

Distinct59
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0049868088
Minimum-0
Maximum0.90909091
Zeros20956
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-05-28T11:01:01.754574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median0
Q3-0
95-th percentile-0
Maximum0.90909091
Range0.90909091
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.044609877
Coefficient of variation (CV)8.945576
Kurtosis105.95238
Mean0.0049868088
Median Absolute Deviation (MAD)0
Skewness9.863123
Sum105.97467
Variance0.0019900411
MonotonicityNot monotonic
2023-05-28T11:01:01.880545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 20956
98.6%
0.3584229391 42
 
0.2%
0.278551532 22
 
0.1%
0.6329113924 19
 
0.1%
0.5063291139 18
 
0.1%
0.27100271 17
 
0.1%
0.2949852507 14
 
0.1%
0.4016064257 14
 
0.1%
0.1792114695 13
 
0.1%
0.3039513678 12
 
0.1%
Other values (49) 124
 
0.6%
ValueCountFrequency (%)
-0 20956
98.6%
0.08130081301 1
 
< 0.1%
0.1253132832 1
 
< 0.1%
0.135501355 3
 
< 0.1%
0.1381692573 1
 
< 0.1%
0.1394052045 1
 
< 0.1%
0.1474926254 1
 
< 0.1%
0.1517450683 2
 
< 0.1%
0.1519756839 1
 
< 0.1%
0.1522070015 1
 
< 0.1%
ValueCountFrequency (%)
0.9090909091 1
 
< 0.1%
0.7751937984 1
 
< 0.1%
0.6329113924 1
 
< 0.1%
0.6329113924 19
0.1%
0.5917159763 2
 
< 0.1%
0.5586592179 1
 
< 0.1%
0.5063291139 18
0.1%
0.5025125628 1
 
< 0.1%
0.5020080321 2
 
< 0.1%
0.4587155963 1
 
< 0.1%

display_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21239 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Length

2023-05-28T11:01:02.006063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:02.114055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

display_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21014 
1
 
237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Length

2023-05-28T11:01:02.179921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:02.274172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

display_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21089 
1
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Length

2023-05-28T11:01:02.353322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:02.447575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

display_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21228 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Length

2023-05-28T11:01:02.526088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:02.621015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

display_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21238 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Length

2023-05-28T11:01:02.699174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:02.793386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

display_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21135 
1
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Length

2023-05-28T11:01:02.871954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:02.966051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

display_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20995 
1
 
256

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Length

2023-05-28T11:01:03.044584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:03.138730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

display_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21165 
1
 
86

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Length

2023-05-28T11:01:03.217238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:03.295349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

display_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21240 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Length

2023-05-28T11:01:03.373866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:03.468314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

mailer_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18091 
1
3160 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Length

2023-05-28T11:01:03.546588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:03.640845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

mailer_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21225 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Length

2023-05-28T11:01:03.719356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:03.817189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

mailer_D
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20215 
1
 
1036

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Length

2023-05-28T11:01:03.892020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:03.986578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

mailer_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21076 
1
 
175

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Length

2023-05-28T11:01:04.065149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:04.143799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

mailer_J
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21139 
1
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Length

2023-05-28T11:01:04.238896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:04.333023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

marital_status_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
10861 
1
10390 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Length

2023-05-28T11:01:04.411138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:04.489696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring characters

ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

marital_status_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18586 
1
2665 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Length

2023-05-28T11:01:04.568333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:04.662476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
1
14585 
0
6666 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Length

2023-05-28T11:01:04.740998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:04.835513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring characters

ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20929 
1
 
322

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Length

2023-05-28T11:01:04.918311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:05.018446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21141 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Length

2023-05-28T11:01:05.088130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:05.182265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

homeowner_Renter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20447 
1
 
804

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Length

2023-05-28T11:01:05.260785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:05.355567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19934 
1
 
1317

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Length

2023-05-28T11:01:05.434030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:05.519160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
14980 
1
6271 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Length

2023-05-28T11:01:05.590879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:05.685409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring characters

ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
14578 
1
6673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Length

2023-05-28T11:01:05.764110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:05.858199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring characters

ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18112 
1
3139 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Length

2023-05-28T11:01:05.936832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:06.030966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18644 
1
2607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Length

2023-05-28T11:01:06.120535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:06.204291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

kid_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
17867 
1
3384 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Length

2023-05-28T11:01:06.298433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:06.408420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

kid_category_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19157 
1
2094 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Length

2023-05-28T11:01:06.943769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:07.037403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

kid_category_3+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19078 
1
2173 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Length

2023-05-28T11:01:07.115512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:07.194033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
1
13600 
0
7651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Length

2023-05-28T11:01:07.272561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:07.366702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring characters

ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

age_ordinal
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3454426
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-05-28T11:01:07.429596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1672614
Coefficient of variation (CV)0.3489109
Kurtosis-0.18576759
Mean3.3454426
Median Absolute Deviation (MAD)1
Skewness0.20537265
Sum71094
Variance1.3624991
MonotonicityNot monotonic
2023-05-28T11:01:07.507717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 7389
34.8%
3 5886
27.7%
2 4503
21.2%
5 1424
 
6.7%
6 1141
 
5.4%
1 908
 
4.3%
ValueCountFrequency (%)
1 908
 
4.3%
2 4503
21.2%
3 5886
27.7%
4 7389
34.8%
5 1424
 
6.7%
6 1141
 
5.4%
ValueCountFrequency (%)
6 1141
 
5.4%
5 1424
 
6.7%
4 7389
34.8%
3 5886
27.7%
2 4503
21.2%
1 908
 
4.3%

income_ordinal
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.366242
Minimum10
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-05-28T11:01:07.601860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q139.5
median62
Q3112
95-th percentile162
Maximum250
Range240
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation52.804688
Coefficient of variation (CV)0.67381932
Kurtosis0.70146606
Mean78.366242
Median Absolute Deviation (MAD)25
Skewness1.0405415
Sum1665361
Variance2788.335
MonotonicityNot monotonic
2023-05-28T11:01:07.680384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
62 4761
22.4%
39.5 3827
18.0%
87 2686
12.6%
162 1827
 
8.6%
112 1709
 
8.0%
29.5 1687
 
7.9%
137 1590
 
7.5%
19.5 1160
 
5.5%
10 1108
 
5.2%
250 375
 
1.8%
Other values (2) 521
 
2.5%
ValueCountFrequency (%)
10 1108
 
5.2%
19.5 1160
 
5.5%
29.5 1687
 
7.9%
39.5 3827
18.0%
62 4761
22.4%
87 2686
12.6%
112 1709
 
8.0%
137 1590
 
7.5%
162 1827
 
8.6%
187 375
 
1.8%
ValueCountFrequency (%)
250 375
 
1.8%
224.5 146
 
0.7%
187 375
 
1.8%
162 1827
 
8.6%
137 1590
 
7.5%
112 1709
 
8.0%
87 2686
12.6%
62 4761
22.4%
39.5 3827
18.0%
29.5 1687
 
7.9%

hhsize_ordinal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
2.0
7958 
1.0
5971 
3.0
3331 
5.0
2087 
4.0
1904 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63753
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 7958
37.4%
1.0 5971
28.1%
3.0 3331
15.7%
5.0 2087
 
9.8%
4.0 1904
 
9.0%

Length

2023-05-28T11:01:07.774517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:07.869065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 7958
37.4%
1.0 5971
28.1%
3.0 3331
15.7%
5.0 2087
 
9.8%
4.0 1904
 
9.0%

Most occurring characters

ValueCountFrequency (%)
. 21251
33.3%
0 21251
33.3%
2 7958
 
12.5%
1 5971
 
9.4%
3 3331
 
5.2%
5 2087
 
3.3%
4 1904
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42502
66.7%
Other Punctuation 21251
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21251
50.0%
2 7958
 
18.7%
1 5971
 
14.0%
3 3331
 
7.8%
5 2087
 
4.9%
4 1904
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 21251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63753
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 21251
33.3%
0 21251
33.3%
2 7958
 
12.5%
1 5971
 
9.4%
3 3331
 
5.2%
5 2087
 
3.3%
4 1904
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 21251
33.3%
0 21251
33.3%
2 7958
 
12.5%
1 5971
 
9.4%
3 3331
 
5.2%
5 2087
 
3.3%
4 1904
 
3.0%

campaign_6.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21244 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Length

2023-05-28T11:01:07.963426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:08.058037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

campaign_8.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19628 
1
 
1623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Length

2023-05-28T11:01:08.136702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:08.230842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

campaign_13.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19596 
1
 
1655

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Length

2023-05-28T11:01:08.294053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:08.388533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

campaign_18.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19639 
1
 
1612

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Length

2023-05-28T11:01:08.467450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:08.561885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

campaign_29.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21107 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Length

2023-05-28T11:01:08.624880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:08.725023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

campaign_30.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21248 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Length

2023-05-28T11:01:08.797027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:08.891277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
16358 
1
4893 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Length

2023-05-28T11:01:08.954304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:09.048399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring characters

ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

description_TypeB
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21107 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Length

2023-05-28T11:01:09.126910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:09.226207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

description_TypeC
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21244 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Length

2023-05-28T11:01:09.299713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T11:01:09.378277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Interactions

2023-05-28T11:00:56.527971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:50.732428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.602461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.416102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.186420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:54.116161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:55.360755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:56.685496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:50.858768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.724773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.541615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.328322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:54.257288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:55.518106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:56.843000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:50.984301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.849700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.651923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.454409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:54.430130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:55.692128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:57.377606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.102416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.975080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.761792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.564162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:54.587936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:55.834990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:57.567387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.220505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.084936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.872083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.721574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:54.777811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:56.023991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:57.725900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.346580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.203175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.982196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.848123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:54.936082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:56.213314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:57.911001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:51.472780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:52.306217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.076339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:53.989515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:55.139646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T11:00:56.370119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-28T11:01:09.519367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0shelf_pricepct_discpct_retail_discpct_coupon_discage_ordinalincome_ordinalfirst_purchasedisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Hmailer_Jmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownhhsize_ordinalcampaign_6.0campaign_8.0campaign_13.0campaign_18.0campaign_29.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
Unnamed: 01.000-0.037-0.021-0.0240.015-0.0480.1150.0590.0020.0250.0370.0270.0350.0470.0180.0260.0230.0350.0190.0430.0460.0140.1880.1300.1880.3040.1000.1720.1360.1640.1310.1610.1910.1740.1850.1880.1620.1710.0300.0500.0230.0600.0750.0000.0400.0750.030
shelf_price-0.0371.0000.5230.5100.129-0.0030.0080.1180.0490.0410.2890.0310.0000.0380.0470.2250.0090.1300.1790.1270.0270.0360.0960.0450.1040.0450.0860.0680.1180.0690.1020.0750.1040.0470.1020.1700.1170.1130.0510.0450.0210.0670.0420.0670.0360.0420.051
pct_disc-0.0210.5231.0000.9840.201-0.014-0.0360.0640.0540.0790.1850.0240.0580.0990.1500.1420.0000.4350.1260.4640.2320.0760.0550.0510.0720.0760.0440.0370.1040.1030.0990.0840.0840.0570.0720.0940.1350.0760.0480.0980.0690.0720.0990.0000.0940.0990.048
pct_retail_disc-0.0240.5100.9841.0000.042-0.013-0.0390.0830.0500.1190.1890.0260.0320.0980.1600.1440.0000.4690.1360.5440.2350.0820.0540.0560.0800.0780.0490.0330.1180.0980.1000.0800.0870.0540.0670.1070.1350.0780.0460.1040.0720.0790.1000.0000.1120.1000.046
pct_coupon_disc0.0150.1290.2010.0421.000-0.0100.0070.0330.0170.0140.0190.0000.0540.0180.0000.0150.0000.0200.0340.0110.0000.0000.0550.0220.0350.0000.0360.0100.0110.0450.0180.0170.0290.0090.0130.0810.0500.0440.0620.0170.0260.0100.0000.0000.0010.0000.062
age_ordinal-0.048-0.003-0.014-0.013-0.0101.000-0.0210.0510.0000.0170.0290.0150.0210.0310.0380.0240.0050.0110.0060.0450.0150.0000.2200.1620.1930.1230.0610.1750.1050.2240.1670.2130.1590.1080.1620.1380.2290.1430.0410.0320.0550.0250.0270.0000.0500.0270.041
income_ordinal0.1150.008-0.036-0.0390.007-0.0211.0000.1720.0170.0270.0260.0310.0170.0890.0300.0630.0000.0450.0000.0470.0270.0490.3050.1600.3450.1840.0690.1270.1710.1990.1530.1960.1700.1830.1840.2340.2120.2120.0130.0210.0500.0340.0550.0000.0340.0550.013
first_purchase0.0590.1180.0640.0830.0330.0510.1721.0000.0000.0000.0180.0000.0000.0000.0280.0000.0000.0080.0040.0160.0140.0000.0510.0370.0550.0240.0260.0410.0220.0000.0300.0260.0360.0060.0150.0200.0150.0240.0100.0300.0280.0390.0000.0100.0620.0000.010
display_10.0020.0490.0540.0500.0170.0000.0170.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0660.0110.0000.0140.0000.0000.0000.0000.0000.0070.0000.0230.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.000
display_20.0250.0410.0790.1190.0140.0170.0270.0000.0001.0000.0000.0000.0000.0000.0070.0000.0000.0860.0000.1080.0270.0190.0150.0000.0000.0000.0000.0080.0090.0000.0140.0110.0000.0000.0000.0180.0000.0190.0000.0100.0000.0160.0000.0000.0030.0000.000
display_30.0370.2890.1850.1890.0190.0290.0260.0180.0000.0001.0000.0000.0000.0000.0020.0000.0000.0240.0000.0170.0000.0000.0000.0000.0030.0000.0000.0340.0250.0160.0250.0010.0210.0000.0460.0000.0290.0500.0000.0000.0000.0000.0000.0000.0000.0000.000
display_40.0270.0310.0240.0260.0000.0150.0310.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0040.0000.0070.0000.0030.0000.0000.0020.0130.0000.0150.0100.0050.0000.0090.0220.0000.0000.0010.0000.0000.0000.0000.0000.000
display_50.0350.0000.0580.0320.0540.0210.0170.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0170.0080.0170.0000.0090.0000.0000.0120.0080.0030.0150.0040.0000.0000.0150.0280.0000.0000.0000.0000.0000.0000.0000.0000.000
display_60.0470.0380.0990.0980.0180.0310.0890.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0700.0000.0310.0170.0000.0040.0300.0150.0000.0000.0000.0070.0000.0050.0120.0210.0000.0000.0000.0050.0000.0000.0140.0340.0160.0000.0000.0200.0000.000
display_70.0180.0470.1500.1600.0000.0380.0300.0280.0000.0070.0020.0000.0000.0001.0000.0000.0000.0790.0130.1140.0590.0000.0060.0160.0000.0000.0000.0050.0000.0000.0130.0080.0000.0090.0090.0000.0000.0000.0000.0000.0120.0200.0000.0000.0000.0000.000
display_90.0260.2250.1420.1440.0150.0240.0630.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0110.0000.0110.0000.0000.0000.0080.0020.0000.0000.0230.0180.0200.0250.0000.0060.0270.0130.0000.0290.0180.0000.0000.0000.0090.0000.0000.0000.0000.000
display_A0.0230.0090.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0120.0000.0000.0000.0000.0110.0000.0110.0000.0000.0000.0000.0000.0000.000
mailer_A0.0350.1300.4350.4690.0200.0110.0450.0080.0000.0860.0240.0190.0000.0700.0790.0110.0001.0000.0110.0940.0370.0290.0160.0000.0270.0000.0000.0030.0000.0000.0000.0150.0040.0210.0160.0040.0000.0300.0000.0240.0760.0250.0000.0000.0140.0000.000
mailer_C0.0190.1790.1260.1360.0340.0060.0000.0040.0000.0000.0000.0000.0000.0000.0130.0000.0000.0111.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0120.0120.0000.0000.0100.0210.0000.0190.0280.0000.0030.0030.0170.0000.0000.0000.0000.000
mailer_D0.0430.1270.4640.5440.0110.0450.0470.0160.0000.1080.0170.0000.0150.0310.1140.0110.0000.0940.0001.0000.0180.0130.0000.0050.0000.0000.0000.0200.0000.0000.0150.0000.0020.0060.0090.0110.0040.0260.0000.0010.0050.0640.0160.0000.0410.0160.000
mailer_H0.0460.0270.2320.2350.0000.0150.0270.0140.0060.0270.0000.0000.0000.0170.0590.0000.0000.0370.0000.0181.0000.0000.0120.0050.0030.0040.0000.0150.0140.0000.0110.0040.0070.0130.0180.0110.0020.0250.0000.1220.0250.0000.0000.0000.0550.0000.000
mailer_J0.0140.0360.0760.0820.0000.0000.0490.0000.0660.0190.0000.0000.0000.0000.0000.0000.0000.0290.0000.0130.0001.0000.0030.0000.0000.0070.0000.0060.0100.0120.0060.0060.0000.0030.0000.0220.0190.0260.0000.0180.0190.0180.0000.0000.0380.0000.000
marital_status_A0.1880.0960.0550.0540.0550.2200.3050.0510.0110.0150.0000.0040.0170.0040.0060.0000.0000.0160.0000.0000.0120.0031.0000.3700.4310.0660.0680.0200.0580.3720.1560.2580.2600.1070.1400.2490.3260.6330.0000.0040.0080.0200.0000.0000.0090.0000.000
marital_status_B0.1300.0450.0510.0560.0220.1620.1600.0370.0000.0000.0000.0000.0080.0300.0160.0080.0000.0000.0000.0050.0050.0000.3701.0000.0110.0000.0880.1760.1930.2210.0740.0840.2200.0790.0000.0890.1150.2510.0000.0060.0280.0130.0000.0000.0130.0000.000
homeowner_Homeowner0.1880.1040.0720.0800.0350.1930.3450.0550.0140.0000.0030.0070.0170.0150.0000.0020.0000.0270.0000.0000.0030.0000.4310.0111.0000.1830.1060.2930.0820.2040.2310.3350.1630.0000.0810.1600.1480.4440.0020.0160.0200.0000.0370.0000.0000.0370.002
homeowner_Probable Owner0.3040.0450.0760.0780.0000.1230.1840.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0070.0660.0000.1831.0000.0000.0230.0300.0570.0320.2030.0450.0250.0400.0410.0710.1210.0000.0300.0080.0070.0040.0000.0180.0040.000
homeowner_Probable Renter0.1000.0860.0440.0490.0360.0610.0690.0260.0000.0000.0000.0030.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0880.1060.0001.0000.0110.0160.0450.0480.0520.0230.0300.0220.0220.0530.1130.0000.0070.0130.0020.0000.0000.0000.0000.000
homeowner_Renter0.1720.0680.0370.0330.0100.1750.1270.0410.0000.0080.0340.0000.0000.0000.0050.0230.0000.0030.0000.0200.0150.0060.0200.1760.2930.0230.0111.0000.2510.0370.0320.0240.0310.0130.1130.0120.0900.1640.0000.0100.0410.0000.0100.0000.0340.0100.000
hhcomp_1 Adult Kids0.1360.1180.1040.1180.0110.1050.1710.0220.0000.0090.0250.0000.0000.0070.0000.0180.0000.0000.0080.0000.0140.0100.0580.1930.0820.0300.0160.2511.0000.1660.1740.1070.0960.1030.2130.2080.3420.2420.0000.0250.0080.0140.0190.0000.0330.0190.000
hhcomp_2 Adults Kids0.1640.0690.1030.0980.0450.2240.1990.0000.0000.0000.0160.0020.0120.0000.0000.0200.0040.0000.0120.0000.0000.0120.3720.2210.2040.0570.0450.0370.1661.0000.4380.2690.2420.5510.3530.3530.8630.8930.0000.0050.0230.0180.0230.0120.0050.0230.000
hhcomp_2 Adults No Kids0.1310.1020.0990.1000.0180.1670.1530.0300.0070.0140.0250.0130.0080.0050.0130.0250.0000.0000.0120.0150.0110.0060.1560.0740.2310.0320.0480.0320.1740.4381.0000.2810.2530.2940.2230.2280.5070.8740.0070.0310.0240.0070.0020.0000.0420.0020.007
hhcomp_Single Female0.1610.0750.0840.0800.0170.2130.1960.0260.0000.0110.0010.0000.0030.0120.0080.0000.0000.0150.0000.0000.0040.0060.2580.0840.3350.2030.0520.0240.1070.2690.2811.0000.1550.1810.1370.1400.3120.5070.0170.0000.0140.0000.0090.0000.0040.0090.017
hhcomp_Single Male0.1910.1040.0840.0870.0290.1590.1700.0360.0230.0000.0210.0150.0150.0210.0000.0060.0120.0040.0000.0020.0070.0000.2600.2200.1630.0450.0230.0310.0960.2420.2530.1551.0000.1620.1230.1260.2800.4850.0000.0110.0000.0180.0000.0000.0220.0000.000
kid_category_10.1740.0470.0570.0540.0090.1080.1830.0060.0000.0000.0000.0100.0040.0000.0090.0270.0000.0210.0100.0060.0130.0030.1070.0790.0000.0250.0300.0130.1030.5510.2940.1810.1621.0000.1440.1470.5800.8940.0070.0000.0130.0090.0510.0210.0000.0510.007
kid_category_20.1850.1020.0720.0670.0130.1620.1840.0150.0000.0000.0460.0050.0000.0000.0090.0130.0000.0160.0210.0090.0180.0000.1400.0000.0810.0400.0220.1130.2130.3530.2230.1370.1230.1441.0000.1110.4410.9070.0000.0090.0300.0210.0130.0000.0110.0130.000
kid_category_3+0.1880.1700.0940.1070.0810.1380.2340.0200.0000.0180.0000.0000.0000.0000.0000.0000.0000.0040.0000.0110.0110.0220.2490.0890.1600.0410.0220.0120.2080.3530.2280.1400.1260.1470.1111.0000.4500.9790.0000.0240.0000.0210.0260.0000.0280.0260.000
kid_category_None/Unknown0.1620.1170.1350.1350.0500.2290.2120.0150.0000.0000.0290.0090.0150.0050.0000.0290.0000.0000.0190.0040.0020.0190.3260.1150.1480.0710.0530.0900.3420.8630.5070.3120.2800.5800.4410.4501.0000.9670.0000.0190.0290.0040.0090.0090.0250.0090.000
hhsize_ordinal0.1710.1130.0760.0780.0440.1430.2120.0240.0130.0190.0500.0220.0280.0000.0000.0180.0110.0300.0280.0260.0250.0260.6330.2510.4440.1210.1130.1640.2420.8930.8740.5070.4850.8940.9070.9790.9671.0000.0160.0370.0360.0310.0570.0240.0420.0570.016
campaign_6.00.0300.0510.0480.0460.0620.0410.0130.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0070.0170.0000.0070.0000.0000.0000.0161.0000.0000.0000.0000.0000.0000.0000.0000.929
campaign_8.00.0500.0450.0980.1040.0170.0320.0210.0300.0000.0100.0000.0000.0000.0140.0000.0000.0110.0240.0030.0010.1220.0180.0040.0060.0160.0300.0070.0100.0250.0050.0310.0000.0110.0000.0090.0240.0190.0370.0001.0000.0830.0820.0220.0000.5260.0220.000
campaign_13.00.0230.0210.0690.0720.0260.0550.0500.0280.0000.0000.0000.0010.0000.0340.0120.0000.0000.0760.0030.0050.0250.0190.0080.0280.0200.0080.0130.0410.0080.0230.0240.0140.0000.0130.0300.0000.0290.0360.0000.0831.0000.0830.0220.0000.5310.0220.000
campaign_18.00.0600.0670.0720.0790.0100.0250.0340.0390.0000.0160.0000.0000.0000.0160.0200.0090.0000.0250.0170.0640.0000.0180.0200.0130.0000.0070.0020.0000.0140.0180.0070.0000.0180.0090.0210.0210.0040.0310.0000.0820.0831.0000.0220.0000.5240.0220.000
campaign_29.00.0750.0420.0990.1000.0000.0270.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0370.0040.0000.0100.0190.0230.0020.0090.0000.0510.0130.0260.0090.0570.0000.0220.0220.0221.0000.0000.0440.9970.000
campaign_30.00.0000.0670.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0210.0000.0000.0090.0240.0000.0000.0000.0000.0001.0000.0160.0000.000
description_TypeA0.0400.0360.0940.1120.0010.0500.0340.0620.0000.0030.0000.0000.0000.0200.0000.0000.0000.0140.0000.0410.0550.0380.0090.0130.0000.0180.0000.0340.0330.0050.0420.0040.0220.0000.0110.0280.0250.0420.0000.5260.5310.5240.0440.0161.0000.0440.000
description_TypeB0.0750.0420.0990.1000.0000.0270.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0370.0040.0000.0100.0190.0230.0020.0090.0000.0510.0130.0260.0090.0570.0000.0220.0220.0220.9970.0000.0441.0000.000
description_TypeC0.0300.0510.0480.0460.0620.0410.0130.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0070.0170.0000.0070.0000.0000.0000.0160.9290.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-05-28T11:00:58.293192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-28T11:00:59.914927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Hmailer_Jmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_ordinalincome_ordinalhhsize_ordinalcampaign_6.0campaign_8.0campaign_13.0campaign_18.0campaign_29.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
016True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
117True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
218True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
319True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
420True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
521True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
622False0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
723True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
824False0.790.2405060.240506-0.0000000001000001010000010000014.062.02.0000000000
925True0.790.2405060.240506-0.0000000000000001010000010000014.062.02.0000000000
Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Hmailer_Jmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_ordinalincome_ordinalhhsize_ordinalcampaign_6.0campaign_8.0campaign_13.0campaign_18.0campaign_29.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
2124134379True0.790.2405060.240506-0.0000000000000000010000010000012.062.02.0000000000
2124234380True0.790.2405060.240506-0.0000000000000000010000010000012.062.02.0000000000
2124334381True0.790.2405060.240506-0.0000000000000000010000010000012.062.02.0000000000
2124434382True0.790.2405060.240506-0.0000000000000000010000010000012.062.02.0000000000
2124534383True0.790.2405060.240506-0.0000000000000000010000010000012.062.02.0000000000
2124634384True0.790.2405060.240506-0.0000000000000000010000010000012.062.02.0000000000
2124734385True2.690.0743490.074349-0.0000000000000000010000010000012.062.02.0000100100
2124834386True2.790.1039430.103943-0.0000000000000000000000100010002.010.03.0000000000
2124934387True2.790.1039430.103943-0.0000000000000000000000100010002.010.03.0000000000
2125034388True3.990.2481200.248120-0.0000000000000000000000100010002.010.03.0000000000